How to Secure Software in the Generative AI Era | Risks & Best Practices

Recent research by Microsoft shows that developers using GenAI completed 26% more tasks, increased code commits by 13.5%, and improved build frequency by 38.4%. These numbers confirm what many already suspect: The GenAI-powered coding revolution is here to stay.

The Productivity-Security Trade-Off

While the benefits are clear, GenAI introduces a critical challenge: security. The industry is pushing for secure code training and safer development practices, but GenAI accelerates the coding cycle beyond traditional safeguards.

Historically, developers took weeks or months to write and vet code. But DevOps and DevSecOps introduced agile cycles that required security to be baked into the pipeline. Now, GenAI has further compressed timelines—creating more code, faster. However, the vulnerability density remains the same, since many GenAI models are trained on open-source datasets that may include insecure patterns.

This leads to a troubling reality: more code = more vulnerabilities.

What the Research Says

Several studies highlight the risks of overreliance on GenAI:

• NYU researchers found that 40% of Copilot-generated code contains known vulnerabilities.
• Wuhan University discovered flaws in 30% of Python and 24% of JavaScript AI-generated snippets.
• A Stanford study showed developers were more likely to write insecure code when assisted by LLMs, yet felt overly confident in its security.

These findings reveal a key psychological flaw: developers often trust GenAI-generated code more than they should.

How Can Generative AI Models Assist Developers in the Context of Code Review?

GenAI tools are not inherently risky—they’re just underutilized in the right areas. Instead of only relying on them for generation, companies can integrate GenAI into code review and security analysis workflows. By guiding GenAI to identify risks, explain vulnerabilities, and suggest remediations, developers can turn the tide.

To reduce risk:

• Use curated datasets for training GenAI tools.
• Design secure prompts that prioritize best practices.
• Educate teams with secure code training programs.
• Integrate AI-assisted vulnerability scanning and remediation into CI/CD pipelines.

The Role of AI in Security Automation

The velocity at which GenAI introduces vulnerabilities is outpacing human remediation capacity. According to Veracode’s SoSS Report, only 20% of applications fix more than 10% of identified flaws monthly. Meanwhile, security debt continues to rise—older flaws remain unpatched, increasing compliance and quality risks.

This is where AI-assisted security comes in:

• Vulnerability Detection: AI tools can scan large volumes of GenAI-generated code.
• Automated Remediation: Suggestions and code fixes generated in real-time.
• Security Debt Reduction: Address legacy issues at scale.

Conclusion

GenAI is a game-changer for software development—but with great speed comes greater responsibility. Organizations must not only embrace AI for coding but also for security.

By combining secure code training, AI-driven code reviews, and automated security tooling, developers can continue to benefit from GenAI’s productivity while protecting software integrity.

In the GenAI era, the goal is no longer just faster code, but secure and scalable code—built with intelligence, maintained with diligence.

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